Drew Wallace has completed
Single-Cell RNA-Seq with Bioconductor in R
Start course For Free4 hr
4,100 XP
Loved by learners at thousands of companies
Course Description
Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecedented opportunity to investigate fundamental biological questions at the cellular level, such as stem cell differentiation or the discovery and characterization of rare cell types. The majority of the computational methods to analyze single-cell RNA-Seq data are implemented in R making it a natural tool to start working with single-cell transcriptomic data. Using real single-cell datasets, this course provides a step-by-step tutorial to the methodology and associated R packages for the following four main tasks: (1) normalization, (2) dimensionality reduction, (3) clustering, (4) differential expression analysis.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.- 1
What is Single-Cell RNA-Seq?
FreeIn Chapter 1, you will learn what single-cell RNA-Seq is and why it is a such a powerful technique. By the end of this chapter, you'll also know how to load, create, and access single-cell datasets in R.
What is Single Cell RNA-Seq, and why is it useful?50 xpBulk versus Single-cell RNA-Seq50 xpExplore a toy scRNA-Seq dataset100 xpCompute cell coverage100 xpTypical workflow50 xpGC content100 xpLibrary size50 xpNesting between batches and biology100 xpLoad, create, and access single-cell datasets in R50 xpSCE object from counts matrix100 xpSCE object from SummarizedExperiment100 xpLoad a single-cell dataset in R100 xp - 2
Quality Control and Normalization
In Chapter 2, we go over the first steps of the workflow to analyze single-cell RNA-seq data, which include quality control and normalization. These two steps should get all the technical issues and biases out of the way so that in the next chapters we can focus on the biological signal of interest.
Quality Control50 xpExplore Tung dataset100 xpCalculate QC metrics100 xpFilter cells with small library size100 xpQuality Control (continued)50 xpFilter cells by number of expressed genes100 xpUse of positive controls100 xpFilter genes mainly not expressed100 xpNormalization50 xpBatch effect100 xpCorrelation between PC1 and library size100 xpCompute size factors100 xpNormalize SCE object100 xp - 3
Visualization and Dimensionality Reduction
When studying single-cell data at the cellular level, the number of dimensions is the number of genes. The goal of dimensionality reduction is to reduce the number of dimensions to a smaller number either to visualize the data in 2 dimensions or to prepare the dataset for subsequent steps like clustering.
Mouse Epithelium Dataset50 xpExplore dataset100 xpNested experiment design100 xpCell differentiation50 xpVisualization50 xpPlot PCA of counts100 xpPlot PCA of log counts100 xpPlot t-SNE of log counts100 xpDimensionality Reduction50 xpSubset sce100 xpPerform PCA on log counts100 xpPlot PCA using ggplot100 xp - 4
Cell Clustering and Differential expression analysis
In Chapter 4, we cluster cells with similar gene expression profiles and then perform differential expression (DE) analysis to find genes differentially expressed between known groups of cells. We then visualize DE genes with volcano plots and heatmaps.
Clustering methods for scRNA-Seq50 xpCreate Seurat object100 xpPerform PCA on Seurat object100 xpPerform clustering50 xpRefine clustering50 xpDifferential expression analysis50 xpFit zero-inflated regression using MAST100 xpCreate result table100 xpCompute adjusted p-values100 xpVisualization of DE genes50 xpPlot volcano plot100 xpUnderstand volcano plot50 xpPlot heatmap100 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.collaborators
Join over 17 million learners and start Single-Cell RNA-Seq with Bioconductor in R today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.